公开(公告)号：US11715258B2

公开(公告)日：2023-08-01

申请号：US17243594

申请日：2021-04-29

Applicant: University of Electronic Science and Technology of China

Inventor： Ping Kuang ,  Liang Peng ,  Xiaofeng Gu

IPC: G06T17/00 ,  G06T15/20 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06F18/213 ,  G06F18/21 ,  G06V10/764 ,  G06V10/84 ,  G06V20/64

CPC classification number: G06T17/00 ,  G06F18/213 ,  G06F18/217 ,  G06N3/04 ,  G06N3/08 ,  G06T15/205 ,  G06V10/764 ,  G06V10/82 ,  G06V10/84 ,  G06V20/64 ,  G06T2210/22

Abstract: The present invention provides a method for reconstructing a 3D object based on dynamic graph network, first, obtaining a plurality of feature vectors from 2D image I of an object; then, preparing input data: predefining an initial ellipsoid mesh, obtaining a feature input X by filling initial features and creating a relationship matrix A corresponding to the feature input X; then, inputting the feature input X and corresponding relationship matrix A to a dynamic graph network for integrating and deducing of each vertex's feature, thus new relationship matrix is obtained and used for the later graph convoluting, which improves the initial graph information and makes the initial graph information adapted to the mesh relation of the corresponding object, therefore the accuracy and the effect of 3D object reconstruction have been improved; last, regressing the position, thus the 3D structure of the object is deduced, and the 3D object reconstruction is completed.